Machine Learning In Agriculture: Future-Proof Use Cases

Machine learning is a fast-growing technology with many promising applications in various fields, including marketing, retail, finance, and agriculture. The MarketsandMarkets report estimates that AI in agriculture will reach $4.7 billion in 2028, from $1.7 billion in 2023. The reasons for this are many. By combining reliable data and advanced technologies, machine learning helps farmers make more informed decisions, optimize resource allocation, and reduce crop loss risks. In a nutshell, ML is changing the way how agriculture operates.

In this article, we discuss the most compelling use cases of Machine Learning in agriculture and highlight its benefits, technologies, and potential for the future.

Machine Learning In Agriculture: Future-Proof Use Cases

Why is Machine Learning so important in agriculture?

The agriculture sector is exposed to multiple risks and uncertainties due to weather changes, pest and disease outbreaks, financial issues, and ever-changing market demands. As a result, the industry suffers significant losses in production, waste of resources, and a decline in growth. It is where ML comes into play. The main agricultural challenges that ML technologies solve are:

  • Soil degradation;
  • Water scarcity;
  • Low yields;
  • Pest and disease outbreaks;
  • Weather changes.

ML can help resolve these and other issues through automation and analysis of real-time data. Machine learning can be combined with IoT devices such as autonomous vehicles, sensors, AI harvesting bots, GPS guidance and control systems. IoT-based apps enable farmers to collect real-time data, identify patterns and make informed decisions about weather changes, crops, and pests.

As ML provides valuable insights and improves decision-making processes, it promotes precision agriculture (PA). This term refers to a crop management concept using high-tech sensors and analysis tools to enhance crop yields.Thus, ML allows farmers access to more accurate data to predict demand better, improve decision-making, increase crop yields, lower production costs, and automate agricultural operations. It is only the tip of the iceberg. Let’s look at the other benefits of this technology.

Benefits of Machine Learning in Agriculture

Machine learning technology brings farms the following benefits:

  • Risk management: farmers can use Machine learning to predict which environmental or weather-related factors might harm their business and take steps to mitigate their impact and reduce wastage;
  • Data-driven decision-making: ML models are capable of analyzing vast amounts of historical and real-time data to make informed decisions about plant or animal treatment and crop management;
  • Resource management optimization: ML enables precise and efficient resource management by analyzing diverse data rapidly and optimizing agriculture operations, resulting in fewer costs and time;
  • Safety enhancement: ML systems allow farmers to ensure a healthier and safer working environment by automating many tasks, such as handling pesticides and heavy machinery;
  • Crop resilience enhancement: by analyzing data on weather patterns, disease outbreaks, or insects, ML can provide early warnings and solutions, minimizing crop losses;
  • Market and price prediction: machine learning models can predict crop prices and market demand based on historical trends. As a result, farmers can maximize their profit from crop selection, volume production, and marketing strategies.

The above benefits result in more productive, profitable, and sustainable farming operations. To provide businesses with these capabilities, ML relies on a variety of technologies.

The main Machine learning technologies in agriculture

There are a lot of technologies that make up Machine learning, but the most beneficial for agriculture are: 

Predictive analytics

Being a subset of data science, predictive analytics uses historical data to predict results and improve decision-making. In agriculture, it is used to forecast demand, irrigation requirements, crop yields, and weather patterns. Moreover, farmers can discover risks and opportunities in agriculture, such as early warning signs of crop diseases.

Computer vision

As an AI field, computer vision analyzes data from digital images and videos. Unlike humans, this technology can simultaneously process unlimited amounts of data captured by drones, sensors, or apps. Computer vision systems can analyze images, identify objects, measure them, monitor crop health and soil conditions, and sort and grade the harvest.

Deep learning

This subset of ML is based on artificial neural networks, which allow its algorithms to learn from data similar to humans. Deep learning algorithms process and analyze unstructured data and predict outcomes based on complex data to extract valuable insights. Deep learning is widely used in agriculture to predict crop yields, identify diseases, forecast the weather, and create climate-resilient crops.

Needless to say, that is not all. Machine learning covers deep neural networks (DNNs), natural language processing (NLP), automation, speech recognition, and many more technologies, making it a versatile tool for various applications. Let’s turn now to the most common Machine Learning use cases.

Machine Learning use cases in agriculture

Agriculture applications based on ML can be divided into four main groups:

  • Crop management;
  • Soil management;
  • Water management;
  • Livestock management.

Here is a closer look at each type.

Crop management

Weed detection

Weeds are a serious threat to agriculture because they quickly grow, spread disease, and result in lower yields. In this way, herbicides are commonly used to control weeds. With Machine learning, farmers can detect harmful plants, use herbicides only in affected areas, and modify them to make them more effective against persistent weeds. So, reducing chemical usage results in less environmental damage.

Machine learning can also automate weeding. For example, an ML robot uses pattern recognition to target unwanted plants and, in turn, eliminate weeds.

Disease detection

The other threat to agriculture is crop diseases, which significantly reduce yield quality. Farmers can manually inspect fields looking for signs of crop diseases, which is time-consuming and may be inaccurate. ML can help detect and manage diseases in agriculture in several ways. For example, using a drone or sensors to recognize images, analyzing data, assessing disease risks, recognizing patterns, and implementing early warning systems.

As a result of leveraging Machine learning technologies, farmers can improve disease control strategies, reduce crop losses, and enhance overall agricultural productivity.

Yield prediction

Predicting yields is crucial to maximize profits in the economy. However, forecasting crop yield is challenging because it depends on evaluating multiple factors, such as crops, environment, and their interaction. ML and deep learning models can accurately predict crop yields by considering different factors, such as weather, soil moisture sensors, images, etc. 

Based on that, farmers will be able to make informed decisions for better crop management and cropping during the harvest season.

Water management

Irrigation detection

Irrigation plays a crucial role in water management in agriculture. In addition to optimizing water usage and reducing water consumption, ML models can forecast weather conditions, gauge soil moisture, and adjust for soil temperature. It helps to adjust irrigation schedules accordingly, improving water efficiency automatically. 

Moreover, Machine learning systems can predict how much water a particular crop will require at a specific time. A combination of Machine learning and other advanced technologies can detect leaks in irrigation systems.

Weather monitoring 

Weather monitoring is an essential aspect of agriculture, as the weather impacts crop growth, irrigation needs, and overall farming operations. By providing more accurate predictions and actionable insights about weather, machine learning can improve operational decisions. For example, by analyzing temperature, and soil moisture levels, Machine learning systems can predict when and how much irrigation to use. 

Thus, farmers are able to cut down on the risk of crop damage due to drought and flooding by strategically irrigating them.

Intelligent spraying

Precision spraying uses intelligent software and computer vision to optimize and improve crop protection. The spraying systems use images and sensors to understand the characteristics of plants, soil, and other elements to determine which chemicals to use. It provides a more precise way to apply pesticides and fertilizers based on crop type. 

As a result, farmers are able to optimize spraying operations, reduce costs, and maximize crop yields.

Soil management

Insect detection

Insects threaten crops so early detection of insects prevents yield losses. Machine learning algorithms and computer vision technology can automatically detect insects and help farmers identify infestations more accurately. Also, Machine learning can analyze data from sensors placed in fields, which provide data on temperature, humidity, and other factors that may influence insect activity. A better understanding of insect behavior can help farmers target pest control efforts more effectively.

Thus, farmers can maintain efficient agricultural processes, ensure crop health, maximize resources, and reduce the threat of insects.

Livestock management

Livestock management can greatly benefit from machine learning apps. Farmers can use machine learning to optimize various aspects of livestock management, such as health monitoring, feed management, and breeding programs. For example, machine learning is widely used in animal disease detection. With the help of computer vision, ML-based systems can continuously assess animals’ health and behavior, enabling early detection and prevention of diseases.

As well, IoT and machine learning-based tools can monitor livestock feeding habits. Farmers can use collected data to identify animals with abnormal nutrition patterns, which may indicate a health problem. Ultimately, farmers will understand how different types and food affect animals.

Final Thoughts

Therefore, Machine Learning in agriculture has many applications, from simple analytics to high-tech automated systems. Incorporating innovative technologies and Machine learning in agriculture is a future-proof step towards efficient, smart, and precision farming. ML can help farmers daily in a wide range of applications; however, it cannot replace farmworkers. By harnessing the power of Machine learning algorithms, farmers can minimize environmental impact, manage risks effectively, and optimize overall operations. 

But keep in mind that ML becomes a powerful tool only when properly implemented. Successful implementation and adoption of Machine learning in agriculture may depend on the farm’s size, the available technologies, the level of expertise in ML and many other factors. If you need help, SoftTeco will be able to assist you with adopting machine learning technology smoothly and efficiently. Contact us to learn more about ML solutions and how they can be incorporated into your project.


How is machine learning used in agriculture?

Machine learning in agriculture is widely used to detect diseases, control weeds, predict crop yields, optimize irrigation, manage livestock, and more. Farmers can use machine learning and its technologies to make data-driven decisions about crops or animals, predict demands, manage risks, and optimize internal operations. 

What is the machine learning process?

As an AI branch, ML uses data and algorithms to emulate human learning, improving accuracy over time. As part of this process, a model collects the data needed for learning. The next step is data preparation, which involves converting the data into a usable format and training a machine learning algorithm. Then, a model is tested to see if it works as expected. The final step involves tuning a model to control its behavior and to achieve maximum performance and results.

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